Instructions to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16
# Run inference directly in the terminal:
llama cli -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16
# Run inference directly in the terminal:
llama cli -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16
# Run inference directly in the terminal:
./llama-cli -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Use Docker

docker model run hf.co/amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

LM Studio
Jan

vLLM

How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Ollama
How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with Ollama:
```
ollama run hf.co/amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16
```
Unsloth Desktop

How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with Docker Model Runner:
```
docker model run hf.co/amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16
```

Lemonade

How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-GGUF-uncensored-opus-distill-F16

List all available models

lemonade list

Hermes Agent

How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.5-0.8B postopus-re-re-ablated (GGUF)

This repo contains a Q4_K_M GGUF companion export for the post opus-re-re-ablated Qwen 3.5 0.8B runtime release.

What this is

By taking the finetuned uncensored variant of our Qwen3.5 0.8B model, we further run on an Opus dataset (nohurry/Opus-4.6-Reasoning-3000x-filtered). After that, we run the abliteration once more as the results degrade slightly with respect to refusal rate on harmful prompts.

Static bake details

The baked export used:

refusal direction from: refusal_dir.pt
bake strength: 0.35
scope: all_layers
baked layers: 0-23
modified weights:
- token embeddings
- attention output projections (self_attn.o_proj / linear_attn.out_proj)
- MLP down projections (mlp.down_proj)

See export_manifest.json for the exact build metadata.

Files

qwen3.5-0.8b-ablate-e2-opus46-postopus-runtime.f16.gguf
qwen3.5-0.8b-ablate-e2-opus46-postopus-runtime.Q4_K_M.gguf
comparison_summary.json
refused_prompts_finetuned_ablated.json
train_config.json
export_manifest.json

Reference runtime eval snapshot

These numbers come from the source HF runtime postopus-re-re-ablated release:

Split	Base Qwen	Post-Opus merged	Postopus-re-re-ablated
harmful	54/64	23/64	2/64
harmless	2/64	1/64	2/64

Quick llama.cpp usage

llama-cli -m qwen3.5-0.8b-ablate-e2-opus46-postopus-runtime.Q4_K_M.gguf -p "Explain the concept of market capitalization."

Downloads last month: 1,917

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Quantized

(339)

this model

amkkk
/

Qwen3.5-0.8B-GGUF-uncensored-opus-distill

Qwen3.5-0.8B postopus-re-re-ablated (GGUF)

What this is

Static bake details

Files

Reference runtime eval snapshot

Quick llama.cpp usage

Model tree for amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill

Dataset used to train amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill

Space using amkkk/Qwen3.5-0.8B-GGUF-uncensored-opus-distill 1